Permutation-Based Causal Structure Learning from Interventional Data with Unknown Targets

Wednesday, December 4, 2019 - 4:00pm to 4:30pm

Event Calendar Category

LIDS & Stats Tea

Speaker Name

Chandler Squires



Building and Room Number

LIDS Lounge


Recent innovations in gene editing and gene sequencing technologies have opened the door to developing a more complete understanding of gene regulatory networks, with applications for disease diagnosis, drug development, and biochemical engineering. In particular, the wealth of interventional data from gene knockout experiments presents a ___ source of information for learning causal models of gene regulatory networks directly from gene expression data. However, most existing causal inference methods for learning from interventional data share a common flaw: all intervention targets are assumed to be known in advance. Since gene knockout experiments are known to have off-target effects, these methods may fail on real data. We propose a new method, the Unknown-Target Interventional Greedy Sparsest Permutation (UT-IGSP) algorithm, with consistency guarantees for learning causal DAGs and intervention targets from interventional data with off-target effects. In this talk, I will describe our identifiability results for interventional data with unknown intervention targets, and describe the UT-IGSP algorithm for learning causal models from such data.

Joint work with Yuhao Wang and Caroline Uhler.


Chandler Squires is a PhD student being co-advised by Caroline Uhler and David Sontag. His work focuses broadly on causal inference, including causal structure learning from heterogeneous data sources, active learning for causal structure discovery, and the relationships between causal inference and modern machine learning techniques.